from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()
We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup
time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.
KNeighborsClassifier_brute_force¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | e70d8e7886766d41f30889506baa1e67 | 2cf49829391aa7891e877f2ed070adf0 | 1.955136 | 0.108803 | NaN | 0.000409 | 0.001955 | brute | -1 | 1 | 0.663 | 0.200872 | 0.009173 | 0.687 | 9.733246 | 9.743389 | 0.024 |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 22d5eac4a4146149b35eae6914921514 | 2cf49829391aa7891e877f2ed070adf0 | 2.860174 | 0.070873 | NaN | 0.000280 | 0.002860 | brute | -1 | 5 | 0.757 | 0.203123 | 0.005553 | 0.742 | 14.081008 | 14.086268 | 0.015 |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 2.213675 | 0.038159 | NaN | 0.000361 | 0.002214 | brute | 1 | 100 | 0.882 | 0.243158 | 0.005866 | 0.875 | 9.103871 | 9.106520 | 0.007 |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 0.020864 | 0.002022 | NaN | 0.000038 | 0.020864 | brute | 1 | 100 | 1.000 | 0.009268 | 0.003457 | 0.000 | 2.251128 | 2.402638 | 1.000 |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 2.993470 | 0.054501 | NaN | 0.000267 | 0.002993 | brute | -1 | 100 | 0.882 | 0.243019 | 0.013504 | 0.875 | 12.317837 | 12.336838 | 0.007 |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 0.025731 | 0.002804 | NaN | 0.000031 | 0.025731 | brute | -1 | 100 | 1.000 | 0.008499 | 0.000842 | 0.000 | 3.027632 | 3.042442 | 1.000 |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | efac583623711d395ddae7a1e881ff68 | 2cf49829391aa7891e877f2ed070adf0 | 2.280282 | 0.030067 | NaN | 0.000351 | 0.002280 | brute | 1 | 5 | 0.757 | 0.194381 | 0.006699 | 0.742 | 11.730964 | 11.737929 | 0.015 |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | b79d63bb7670c492de5c3befac58fe29 | 2cf49829391aa7891e877f2ed070adf0 | 1.184285 | 0.008049 | NaN | 0.000676 | 0.001184 | brute | 1 | 1 | 0.663 | 0.189998 | 0.003723 | 0.687 | 6.233156 | 6.234352 | 0.024 |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | e70d8e7886766d41f30889506baa1e67 | 88843f54689e3271092f70126e1de585 | 1.733147 | 0.027726 | NaN | 0.000009 | 0.001733 | brute | -1 | 1 | 0.896 | 0.030211 | 0.002243 | 0.967 | 57.368840 | 57.526685 | 0.071 |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 22d5eac4a4146149b35eae6914921514 | 88843f54689e3271092f70126e1de585 | 2.847563 | 0.058336 | NaN | 0.000006 | 0.002848 | brute | -1 | 5 | 0.922 | 0.031710 | 0.000728 | 0.974 | 89.799672 | 89.823318 | 0.052 |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 16f94fc93942ff7ece860c9b0f64645f | 88843f54689e3271092f70126e1de585 | 2.135111 | 0.014854 | NaN | 0.000007 | 0.002135 | brute | 1 | 100 | 0.929 | 0.072381 | 0.003462 | 0.975 | 29.498210 | 29.531942 | 0.046 |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2d5f2b2c02d77766434d9e5033b4a76c | 88843f54689e3271092f70126e1de585 | 2.811007 | 0.039034 | NaN | 0.000006 | 0.002811 | brute | -1 | 100 | 0.929 | 0.069442 | 0.001590 | 0.975 | 40.479764 | 40.490368 | 0.046 |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | efac583623711d395ddae7a1e881ff68 | 88843f54689e3271092f70126e1de585 | 2.061697 | 0.025369 | NaN | 0.000008 | 0.002062 | brute | 1 | 5 | 0.922 | 0.029766 | 0.000977 | 0.974 | 69.262619 | 69.299887 | 0.052 |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | b79d63bb7670c492de5c3befac58fe29 | 88843f54689e3271092f70126e1de585 | 1.085737 | 0.010496 | NaN | 0.000015 | 0.001086 | brute | 1 | 1 | 0.896 | 0.030876 | 0.001029 | 0.967 | 35.164530 | 35.184059 | 0.071 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.241 | 0.0 | -1 | 1 | 0.051 | 0.004 | 0.216 | 0.217 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.210 | 0.0 | -1 | 5 | 0.049 | 0.001 | 0.227 | 0.227 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.001 | 6.699 | 0.0 | 1 | 100 | 0.048 | 0.001 | 0.248 | 0.248 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.016 | 0.0 | -1 | 100 | 0.050 | 0.003 | 0.230 | 0.230 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.001 | 6.765 | 0.0 | 1 | 5 | 0.048 | 0.001 | 0.249 | 0.249 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.012 | 0.000 | 6.885 | 0.0 | 1 | 1 | 0.048 | 0.001 | 0.240 | 0.240 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.353 | 0.0 | -1 | 1 | 0.010 | 0.000 | 0.477 | 0.477 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.001 | 0.323 | 0.0 | -1 | 5 | 0.008 | 0.000 | 0.587 | 0.588 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.329 | 0.0 | 1 | 100 | 0.009 | 0.000 | 0.534 | 0.534 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.316 | 0.0 | -1 | 100 | 0.009 | 0.000 | 0.567 | 0.567 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.308 | 0.0 | 1 | 5 | 0.009 | 0.001 | 0.591 | 0.592 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.336 | 0.0 | 1 | 1 | 0.009 | 0.000 | 0.518 | 0.519 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.955 | 0.109 | 0.000 | 0.002 | -1 | 1 | 0.201 | 0.009 | 9.733 | 9.743 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.022 | 0.003 | 0.000 | 0.022 | -1 | 1 | 0.009 | 0.002 | 2.441 | 2.530 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.860 | 0.071 | 0.000 | 0.003 | -1 | 5 | 0.203 | 0.006 | 14.081 | 14.086 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.024 | 0.003 | 0.000 | 0.024 | -1 | 5 | 0.009 | 0.003 | 2.581 | 2.690 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.214 | 0.038 | 0.000 | 0.002 | 1 | 100 | 0.243 | 0.006 | 9.104 | 9.107 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.021 | 0.002 | 0.000 | 0.021 | 1 | 100 | 0.009 | 0.003 | 2.251 | 2.403 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.993 | 0.055 | 0.000 | 0.003 | -1 | 100 | 0.243 | 0.014 | 12.318 | 12.337 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.026 | 0.003 | 0.000 | 0.026 | -1 | 100 | 0.008 | 0.001 | 3.028 | 3.042 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.280 | 0.030 | 0.000 | 0.002 | 1 | 5 | 0.194 | 0.007 | 11.731 | 11.738 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.021 | 0.000 | 0.000 | 0.021 | 1 | 5 | 0.008 | 0.000 | 2.638 | 2.640 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.184 | 0.008 | 0.001 | 0.001 | 1 | 1 | 0.190 | 0.004 | 6.233 | 6.234 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.019 | 0.001 | 0.000 | 0.019 | 1 | 1 | 0.008 | 0.000 | 2.445 | 2.448 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.733 | 0.028 | 0.000 | 0.002 | -1 | 1 | 0.030 | 0.002 | 57.369 | 57.527 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.005 | 0.004 | 0.000 | 0.005 | -1 | 1 | 0.001 | 0.000 | 7.129 | 7.202 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.848 | 0.058 | 0.000 | 0.003 | -1 | 5 | 0.032 | 0.001 | 89.800 | 89.823 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.006 | 0.002 | 0.000 | 0.006 | -1 | 5 | 0.001 | 0.000 | 7.132 | 7.283 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.135 | 0.015 | 0.000 | 0.002 | 1 | 100 | 0.072 | 0.003 | 29.498 | 29.532 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 4.276 | 4.304 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.811 | 0.039 | 0.000 | 0.003 | -1 | 100 | 0.069 | 0.002 | 40.480 | 40.490 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.010 | 0.002 | 0.000 | 0.010 | -1 | 100 | 0.001 | 0.000 | 11.488 | 11.556 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.062 | 0.025 | 0.000 | 0.002 | 1 | 5 | 0.030 | 0.001 | 69.263 | 69.300 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 4.808 | 4.831 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.086 | 0.010 | 0.000 | 0.001 | 1 | 1 | 0.031 | 0.001 | 35.165 | 35.184 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.400 | 2.433 | See | See |
KNeighborsClassifier_kd_tree¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | fe8267e25dadb302ed498e50fabf7ef4 | 1eb6cf1a720a225efb91df0b529b0510 | 0.850513 | 1.064081 | NaN | 0.000094 | 0.000851 | kd_tree | -1 | 1 | 0.929 | 0.102109 | 0.003228 | 0.910 | 8.329477 | 8.333637 | 0.019 |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | dc74b969bf622bc24ba3bc62c980983b | 1eb6cf1a720a225efb91df0b529b0510 | 1.079550 | 0.386281 | NaN | 0.000074 | 0.001080 | kd_tree | -1 | 5 | 0.946 | 0.202691 | 0.012861 | 0.941 | 5.326089 | 5.336799 | 0.005 |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 41accfcdcd5c50784bedf6164b63de99 | 1eb6cf1a720a225efb91df0b529b0510 | 5.637007 | 0.623787 | NaN | 0.000014 | 0.005637 | kd_tree | 1 | 100 | 0.951 | 0.575086 | 0.011178 | 0.940 | 9.802020 | 9.803871 | 0.011 |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 022f7445d43bb1dbc24dc3106c03cb93 | 1eb6cf1a720a225efb91df0b529b0510 | 3.259656 | 0.234377 | NaN | 0.000025 | 0.003260 | kd_tree | -1 | 100 | 0.951 | 0.560219 | 0.003594 | 0.940 | 5.818540 | 5.818659 | 0.011 |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 84622ba45e941db642965553529e1941 | 1eb6cf1a720a225efb91df0b529b0510 | 1.595688 | 0.171253 | NaN | 0.000050 | 0.001596 | kd_tree | 1 | 5 | 0.946 | 0.191443 | 0.004462 | 0.941 | 8.335060 | 8.337323 | 0.005 |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | a9455565db1d8e052a783317c99744ff | 1eb6cf1a720a225efb91df0b529b0510 | 0.939030 | 0.361407 | NaN | 0.000085 | 0.000939 | kd_tree | 1 | 1 | 0.929 | 0.105018 | 0.002140 | 0.910 | 8.941601 | 8.943458 | 0.019 |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | fe8267e25dadb302ed498e50fabf7ef4 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.029046 | 0.015580 | NaN | 0.000551 | 0.000029 | kd_tree | -1 | 1 | 0.891 | 0.000500 | 0.000137 | 0.879 | 58.057241 | 60.198328 | 0.012 |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | dc74b969bf622bc24ba3bc62c980983b | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.024962 | 0.001064 | NaN | 0.000641 | 0.000025 | kd_tree | -1 | 5 | 0.911 | 0.000742 | 0.000063 | 0.905 | 33.651294 | 33.772438 | 0.006 |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 41accfcdcd5c50784bedf6164b63de99 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.039243 | 0.011174 | NaN | 0.000408 | 0.000039 | kd_tree | 1 | 100 | 0.894 | 0.005145 | 0.000427 | 0.917 | 7.627563 | 7.653782 | 0.023 |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 022f7445d43bb1dbc24dc3106c03cb93 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.042581 | 0.005913 | NaN | 0.000376 | 0.000043 | kd_tree | -1 | 100 | 0.894 | 0.006052 | 0.001592 | 0.917 | 7.035754 | 7.274997 | 0.023 |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 84622ba45e941db642965553529e1941 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.022603 | 0.000676 | NaN | 0.000708 | 0.000023 | kd_tree | 1 | 5 | 0.911 | 0.000728 | 0.000051 | 0.905 | 31.031421 | 31.106905 | 0.006 |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | a9455565db1d8e052a783317c99744ff | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.020226 | 0.000805 | NaN | 0.000791 | 0.000020 | kd_tree | 1 | 1 | 0.891 | 0.000423 | 0.000029 | 0.879 | 47.792957 | 47.905365 | 0.012 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2.982 | 0.061 | 0.027 | 0.0 | -1 | 1 | 0.782 | 0.060 | 3.813 | 3.824 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.731 | 0.078 | 0.021 | 0.0 | -1 | 5 | 0.768 | 0.016 | 4.856 | 4.857 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.876 | 0.067 | 0.021 | 0.0 | 1 | 100 | 0.765 | 0.029 | 5.066 | 5.070 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 4.019 | 0.131 | 0.020 | 0.0 | -1 | 100 | 0.736 | 0.012 | 5.462 | 5.463 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.941 | 0.122 | 0.020 | 0.0 | 1 | 5 | 0.742 | 0.053 | 5.309 | 5.323 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.780 | 0.108 | 0.021 | 0.0 | 1 | 1 | 0.727 | 0.015 | 5.196 | 5.197 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.001 | 0.018 | 0.0 | -1 | 1 | 0.005 | 0.005 | 0.170 | 0.240 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.026 | 0.0 | -1 | 5 | 0.001 | 0.001 | 0.411 | 0.487 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.026 | 0.0 | 1 | 100 | 0.001 | 0.000 | 0.642 | 0.647 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.027 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.664 | 0.669 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.027 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.568 | 0.578 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.024 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.764 | 0.769 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.851 | 1.064 | 0.000 | 0.001 | -1 | 1 | 0.102 | 0.003 | 8.329 | 8.334 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 9.508 | 9.987 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.080 | 0.386 | 0.000 | 0.001 | -1 | 5 | 0.203 | 0.013 | 5.326 | 5.337 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 7.530 | 7.739 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 5.637 | 0.624 | 0.000 | 0.006 | 1 | 100 | 0.575 | 0.011 | 9.802 | 9.804 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.004 | 0.001 | 0.000 | 0.004 | 1 | 100 | 0.001 | 0.000 | 4.430 | 4.535 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 3.260 | 0.234 | 0.000 | 0.003 | -1 | 100 | 0.560 | 0.004 | 5.819 | 5.819 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.005 | 0.002 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 6.466 | 6.660 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.596 | 0.171 | 0.000 | 0.002 | 1 | 5 | 0.191 | 0.004 | 8.335 | 8.337 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 0.000 | 0.000 | 3.418 | 3.825 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.939 | 0.361 | 0.000 | 0.001 | 1 | 1 | 0.105 | 0.002 | 8.942 | 8.943 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.451 | 3.603 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.029 | 0.016 | 0.001 | 0.000 | -1 | 1 | 0.001 | 0.000 | 58.057 | 60.198 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 21.601 | 23.371 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.025 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 33.651 | 33.772 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 18.448 | 20.076 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.039 | 0.011 | 0.000 | 0.000 | 1 | 100 | 0.005 | 0.000 | 7.628 | 7.654 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 5.071 | 5.541 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.043 | 0.006 | 0.000 | 0.000 | -1 | 100 | 0.006 | 0.002 | 7.036 | 7.275 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 17.727 | 19.093 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.023 | 0.001 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 31.031 | 31.107 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 5.148 | 5.406 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.020 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 47.793 | 47.905 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 5.400 | 5.878 | See | See |
KMeans_tall¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.587 | 0.083 | 30 | 0.027 | 0.0 | random | 0.444 | 0.027 | 1.320 | 1.323 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.659 | 0.025 | 30 | 0.024 | 0.0 | k-means++ | 0.486 | 0.035 | 1.358 | 1.362 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 5.899 | 0.224 | 30 | 0.136 | 0.0 | random | 2.799 | 0.117 | 2.108 | 2.110 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 6.141 | 0.055 | 30 | 0.130 | 0.0 | k-means++ | 2.898 | 0.021 | 2.119 | 2.119 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.009 | 0.000 | random | 0.0 | 0.0 | 8.040 | 13.403 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | random | 0.0 | 0.0 | 8.637 | 12.765 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.009 | 0.000 | k-means++ | 0.0 | 0.0 | 13.116 | 14.579 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | k-means++ | 0.0 | 0.0 | 13.739 | 14.325 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.417 | 0.000 | random | 0.0 | 0.0 | 6.912 | 7.507 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.002 | 0.000 | 30 | 0.001 | 0.002 | random | 0.0 | 0.0 | 12.761 | 13.305 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.386 | 0.000 | k-means++ | 0.0 | 0.0 | 7.271 | 8.201 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | k-means++ | 0.0 | 0.0 | 13.761 | 14.274 | See | See |
KMeans_short¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | adjusted_rand_score_sklearnex | speedup | std_speedup | diff_adjusted_rand_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 3db9c030f488d0e8ff350ae449da4627 | 058b7e3842b587a5c675518f0706f5ee | 0.001948 | 0.000233 | 20 | 0.008213 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.000126 | 0.000453 | 0.000031 | -0.000965 | 4.303341 | 4.313745 | 0.001090 |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | ac9b8cec4749455c0d1cd1a92c150716 | 058b7e3842b587a5c675518f0706f5ee | 0.002082 | 0.000137 | 20 | 0.007685 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.001245 | 0.000481 | 0.000091 | -0.000750 | 4.330115 | 4.406515 | 0.001995 |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 3db9c030f488d0e8ff350ae449da4627 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002702 | 0.000182 | 20 | 0.296097 | 0.000003 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.278733 | 0.000977 | 0.000133 | 0.293767 | 2.765744 | 2.791396 | 0.015034 |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | ac9b8cec4749455c0d1cd1a92c150716 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002908 | 0.000264 | 20 | 0.275116 | 0.000003 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.317011 | 0.001113 | 0.000144 | 0.256968 | 2.613108 | 2.634997 | 0.060044 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 0.087 | 0.003 | 20 | 0.002 | 0.0 | random | 0.030 | 0.002 | 2.925 | 2.930 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 0.251 | 0.008 | 20 | 0.001 | 0.0 | k-means++ | 0.093 | 0.002 | 2.693 | 2.694 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 0.223 | 0.007 | 20 | 0.036 | 0.0 | random | 0.125 | 0.005 | 1.779 | 1.781 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 0.663 | 0.016 | 20 | 0.012 | 0.0 | k-means++ | 0.347 | 0.005 | 1.910 | 1.911 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.008 | 0.000 | random | 0.000 | 0.0 | 4.303 | 4.314 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 0.001 | 0.0 | 20 | 0.000 | 0.001 | random | 0.000 | 0.0 | 12.794 | 13.069 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.008 | 0.000 | k-means++ | 0.000 | 0.0 | 4.330 | 4.407 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 0.002 | 0.0 | 20 | 0.000 | 0.002 | k-means++ | 0.000 | 0.0 | 13.185 | 13.722 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 0.003 | 0.0 | 20 | 0.296 | 0.000 | random | 0.001 | 0.0 | 2.766 | 2.791 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 0.002 | 0.0 | 20 | 0.001 | 0.002 | random | 0.000 | 0.0 | 10.439 | 10.594 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 0.003 | 0.0 | 20 | 0.275 | 0.000 | k-means++ | 0.001 | 0.0 | 2.613 | 2.635 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 0.002 | 0.0 | 20 | 0.000 | 0.002 | k-means++ | 0.000 | 0.0 | 10.662 | 10.861 | See | See |
LogisticRegression¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 09c36ed6521e03974d0c134895f56c01 | 0.000429 | 0.000451 | [20] | 1.862965 | 4.294230e-07 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.56 | 0.000773 | 0.001263 | 0.55 | 0.555826 | 1.064991 | 0.01 |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 7daecaea01e5baa8c862f264e3c1c3b0 | 0.001742 | 0.000170 | [26] | 4.593365 | 1.741643e-05 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.35 | 0.003393 | 0.000188 | 0.28 | 0.513280 | 0.514067 | 0.07 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | 11.369 | 0.511 | [20] | 0.070 | 0.000 | 1.939 | 0.050 | 5.863 | 5.865 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | 0.970 | 0.660 | [26] | 0.082 | 0.001 | 0.722 | 0.049 | 1.344 | 1.347 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | [20] | 1.863 | 0.0 | 0.001 | 0.001 | 0.556 | 1.065 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | [20] | 0.015 | 0.0 | 0.000 | 0.000 | 0.384 | 0.392 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | 0.002 | 0.0 | [26] | 4.593 | 0.0 | 0.003 | 0.000 | 0.513 | 0.514 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | [26] | 1.002 | 0.0 | 0.001 | 0.000 | 0.118 | 0.118 | See | See |
Ridge¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | r2_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | r2_score_sklearnex | speedup | std_speedup | diff_r2_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | a70ae8dc5c059ec88cf27c61016aee2a | 14fae294d530397144c65ba3209bf125 | 0.0099 | 0.000205 | NaN | 8.081125 | 0.00001 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | 0.082567 | 0.016407 | 0.000349 | 0.122191 | 0.603372 | 0.603509 | 0.039624 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | 0.186 | 0.004 | 0.431 | 0.0 | 0.198 | 0.015 | 0.937 | 0.940 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | 1.195 | 0.107 | 0.669 | 0.0 | 0.323 | 0.267 | 3.701 | 4.805 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | 0.01 | 0.000 | 8.081 | 0.0 | 0.016 | 0.0 | 0.603 | 0.604 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | 0.00 | 0.001 | 0.258 | 0.0 | 0.000 | 0.0 | 3.158 | 3.381 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | 0.00 | 0.000 | 5.036 | 0.0 | 0.000 | 0.0 | 0.488 | 0.698 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | 0.00 | 0.000 | 0.013 | 0.0 | 0.000 | 0.0 | 0.742 | 0.782 | See | See |